
import ast
import numpy as np              # NumPy is a Python library used for working with arrays.
import pandas as pd             # Pandas is mainly used for data analysis. Pandas allows importing data from various file formats such as comma-separated values, JSON, SQL, and Microsoft Excel.     
import seaborn as sns           # Seaborn is a library in Python predominantly used for making statistical graphics. Seaborn is a data visualization library built on top of matplotlib .
import matplotlib.pyplot as plt # Matplotlib is a cross-platform, data visualization and graphical plotting library for Python and its numerical extension NumPy.
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import CountVectorizer


# Loading the dataframe
df = pd.read_csv('tmdb_5000_movies.csv')
df_credits = pd.read_csv('tmdb_5000_credits.csv')
df = df.merge(df_credits,on='title')


df.head() # Show the first 5 rows in the data


df_credits.head()


df.shape # Show the number of rows and columns as a tuple (number of rows, number of columns).
# There is 4803 rows and 20 column

(4809, 23)


df.columns # Show name of columns

Index(['budget', 'genres', 'homepage', 'id', 'keywords', 'original_language',
       'original_title', 'overview', 'popularity', 'production_companies',
       'production_countries', 'release_date', 'revenue', 'runtime',
       'spoken_languages', 'status', 'tagline', 'title', 'vote_average',
       'vote_count', 'movie_id', 'cast', 'crew'],
      dtype='object')


#===================================== Column Names and its meaning ====================================
# Budget : The budget in which the movie was made.
# Genre : Type of the movie action , drama , horror ...
# Homepage : Offical page link (Where you can watch the movie).
# Id : ID of the film.
# Keyword : The keywords or tags related to the movie : Words tell the kind of the idea of about what the movie is.
# Original language : The language in which the movie was made.
# Original title : The title of the movie before translation or adaptation.
# Overview : A brief description of the movie.
# Popularity : A numeric quantity specifying the movie popularity.
# Production companies : The production house of the movie.
# Production countries : The country in which it was produced.
# Release date : The date on which it was released.
# Revenue : The worldwide revenue generated by the movie.
# Runtime : The running time of the movie in minutes.
# Status : "Released" or "Rumored".
# Tagline : Movie's tagline.
# Title : Title of the movie.
# Vote average : average ratings the movie recieved.
# Vote count : the count of votes recieved.


df.describe() # It calculate some statistical data like percentile, mean and std of the numerical values of DataFrame.


print("Number of films that have a budget less that 100 : ",len(df[df['budget'] < 100]))
sns.boxplot(df['budget'])

Number of films that have a budget less that 100 :  1062

C:\Users\com\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='budget'>


# Budget
Q1 = df.budget.quantile(0.25)
Q3 = df.budget.quantile(0.75)
IQR = Q3 - Q1
lower_limit = Q1 - 1.5*IQR
upper_limit = Q3 + 1.5*IQR
lower_limit, upper_limit # Upper limit allowed , Lower limit allowed

(-58050000.0, 98830000.0)


df[df["budget"] >upper_limit]


median = df['budget'].median()
df["budget"] = np.where(df["budget"] >upper_limit, median,df['budget'])


df.shape

(4809, 23)


print("Number of films that have a revenue less that 100 : ",len(df[df['revenue'] < 100]))
len(df[df['revenue'] < 100])
sns.boxplot(df['revenue'])

Number of films that have a revenue less that 100 :  1448

C:\Users\com\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='revenue'>


# Revenue
Q1 = df.revenue.quantile(0.25)
Q3 = df.revenue.quantile(0.75)
IQR = Q3 - Q1
lower_limit = Q1 - 1.5*IQR
upper_limit = Q3 + 1.5*IQR
lower_limit, upper_limit

(-139369756.5, 232282927.5)


df[df['revenue']> upper_limit]


median = df['revenue'].median()
df["revenue"] = np.where(df["revenue"] >upper_limit, median,df['revenue'])


df.shape

(4809, 23)


len(df[df['runtime'] == 0])
sns.boxplot(df['runtime'])

C:\Users\com\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='runtime'>


# Runtime
Q1 = df.runtime.quantile(0.25)
Q3 = df.runtime.quantile(0.75)
IQR = Q3 - Q1
lower_limit = Q1 - 1.5*IQR
upper_limit = Q3 + 1.5*IQR
lower_limit, upper_limit

(58.0, 154.0)


df[df['runtime']> upper_limit]


print(len(df[df['runtime']< lower_limit]))
df[df['runtime']< lower_limit]

42


df = df[df['runtime']> lower_limit]
df = df[df['runtime']< upper_limit]


df.shape

(4613, 23)


df.isna().sum() # Returns the number of missing values in each column.

budget                     0
genres                     0
homepage                2950
id                         0
keywords                   0
original_language          0
original_title             0
overview                   1
popularity                 0
production_companies       0
production_countries       0
release_date               0
revenue                    0
runtime                    0
spoken_languages           0
status                     0
tagline                  782
title                      0
vote_average               0
vote_count                 0
movie_id                   0
cast                       0
crew                       0
dtype: int64


df_credits.isna().sum() # There is no null values in this data frame

movie_id    0
title       0
cast        0
crew        0
dtype: int64


df.drop('homepage', inplace=True, axis=1) # Drop Homepage column
df.drop(df[(df['runtime'] == 0)].index, inplace = True) #4. Drop Columns with runtime == 0 , as there is no film have 0 duration
df["tagline"].fillna("", inplace = True) #5. Drop Tagline column
df = df.dropna()  #6. Drop null rows (Overview, Release date, Runtime)


df.isna().sum() # Now, there are not any null values

budget                  0
genres                  0
id                      0
keywords                0
original_language       0
original_title          0
overview                0
popularity              0
production_companies    0
production_countries    0
release_date            0
revenue                 0
runtime                 0
spoken_languages        0
status                  0
tagline                 0
title                   0
vote_average            0
vote_count              0
movie_id                0
cast                    0
crew                    0
dtype: int64


df.shape

(4612, 22)


df.head()


# Let's make our correlation matrix a little prettier
corr_matrix =df.corr()
fig, ax = plt.subplots(figsize=(15, 15))
ax = sns.heatmap(corr_matrix,
                 annot=True,
                 linewidths=0.9,
                 fmt=".2f",
                 cmap="YlGnBu");
bottom, top = ax.get_ylim()
ax.set_ylim(bottom + 0.5, top - 0.5)

(8.5, -0.5)


df['release_year'] = pd.to_datetime(df['release_date']).dt.year # Add a new column with the year of the film


df.drop('release_date', inplace=True, axis=1) # Drop the column of release_date because we replace it with the year column


df.head()


duration_genres = np.array([])
for i in df['runtime']:
    if (i<=40):
        duration_genres = np.append(duration_genres, 0)
    elif (i>40 and i<=75):
        duration_genres = np.append(duration_genres, 1)
    if (i>75):
        duration_genres = np.append(duration_genres, 2)


duration_genres

array([2., 2., 2., ..., 2., 2., 2.])


df['duration_genres'] = duration_genres


df.head()


def convert(obj):
    L = []
    for i in ast.literal_eval(obj):
        L.append(i['name'])
    return L


#FirsT Data Frame :

df['genres'] = df['genres'].apply(convert)
df['keywords'] = df['keywords'].apply(convert)
df['spoken_languages'] = df['spoken_languages'].apply(convert)
df['production_countries'] = df['production_countries'].apply(convert)
df['production_companies'] = df['production_companies'].apply(convert)
#--------------------------------------------------------------------------------------------


df.head()


def convert_actors(obj):
    L = []
    counter = 0
    for i in ast.literal_eval(obj):
        if counter !=8:
            L.append(i['name'])
            counter+=1
        else:
            break
    return L


df['cast'] = df['cast'].apply(convert_actors)


def fetch_director(obj):
    L = []
    for i in ast.literal_eval(obj):
        if i['job'] == 'Director':
            L.append(i['name'])
            break
    return L


df['crew'] = df['crew'].apply(fetch_director)
df['overview'] = df['overview'].apply(lambda x:x.split())


df['genres'] = df['genres'].apply(lambda x:[i.replace(" ","") for i in x])
df['keywords'] = df['keywords'].apply(lambda x:[i.replace(" ","") for i in x])
df['cast'] = df['cast'].apply(lambda x:[i.replace(" ","") for i in x])
df['crew'] = df['crew'].apply(lambda x:[i.replace(" ","") for i in x])


df['features'] = df['overview'] + df['genres'] + df['keywords'] + df['cast'] + df['crew']
df['features'] = df['features'].apply(lambda x:" ".join(x))


df['features'] = df['features'].apply(lambda x:x.lower())


import nltk
from nltk.stem.porter import PorterStemmer
ps = PorterStemmer()

def xStem(txt):
    y = []
    for x in txt.split():
        y.append(ps.stem(x))
    return " ".join(y)


df['features'] = df['features'].apply(xStem)


from sklearn.feature_extraction.text import HashingVectorizer

hv=HashingVectorizer(stop_words="english",n_features=7000)
hv_vector= hv.fit_transform(df['features']).toarray()


similarity = cosine_similarity(hv_vector)


pd.DataFrame(similarity,index=df['title'],columns=df['title'])


distances = similarity[32] ## Similarites for the movie 
sorted(distances,reverse=True)[0:10]

[1.0000000000000002,
 0.3360537729058417,
 0.2832856927186045,
 0.2783349703706405,
 0.26122949691608693,
 0.2545454545454546,
 0.24003840921845832,
 0.23994948963429277,
 0.2397457108377597,
 0.23472626340651012]


def recommend(movie):
    movie_index = df[df['title'] == movie].index[0]
    distances = similarity[movie_index]
    movies_list = sorted(list(enumerate(distances)),reverse=True, key=lambda x:x[1])[1:16]
    mov=[]
    id=[]
    scores=[]
    for i in movies_list:
        mov.append(df.iloc[i[0]].title)
        id.append(df.iloc[i[0]].movie_id)
        scores.append(i[1])
    dic={'movie_id':id,'title':mov,'Similarity Score':scores}
    return pd.DataFrame(dic)


#Histogram to represent runtime of movies
#plt.hist(df['runtime'])
plt.hist(df['runtime'], bins=10)
plt.title('Run Time of movies', fontdict={'fontsize':14, 'color':'brown'})
plt.xlabel("Minutes")
plt.ylabel('Number of movies')
plt.show()


#Scatter plot to compare between Budget and Revenue
plt.scatter(df['budget'], df['revenue'], marker='o', c='blue')

plt.title('Budget vs Revenue', fontdict={'fontsize':14, 'color':'brown'})
plt.xlabel("Budget")
plt.ylabel('revenue')
plt.show()


#countplot to show the top languages in the movies
sns.countplot(x='original_language',data=df,order=pd.value_counts(df['original_language']).iloc[:10].index)
sns.set(rc = {'figure.figsize':(12,8)})
plt.title('Top languages of movies', fontdict={'fontsize':14, 'color':'brown'})

Text(0.5, 1.0, 'Top languages of movies')


language=df.original_language.value_counts()


#Pie chart to represent the top language 
from IPython.display import HTML
import plotly.express as px
plt.figure(figsize=(20,14))
fig= px.pie(df,
            values=language.iloc[:10].values,
            names=language.iloc[:10].index,
            title='Top 10 Languages',
            height=1050,
            width=700)
HTML(fig.to_html())

<Figure size 1440x1008 with 0 Axes>


#This bar used to represent the number of movies that has a specific ratings
fig=px.bar(df,
           x=df['vote_average'].value_counts().index,
           y=df['vote_average'].value_counts(),
           title='Overall Ratings',
           text=df['vote_average'].value_counts(),
           height=700
           )
HTML(fig.to_html())


#The pairwise scatterplot used to show the relation between every columns
import seaborn as sns
# Create the default pairplot
sns.pairplot(df)

<seaborn.axisgrid.PairGrid at 0x133242a1490>


all_countries=[]
for i in df['production_countries']:
    for j in i:
        all_countries.append(j)
#all_countries


from collections import Counter
cont = Counter()

for text in all_countries:
    cont[text] += 1
# See most common ten countries
cont.most_common()

[('United States of America', 3830),
 ('United Kingdom', 605),
 ('Germany', 314),
 ('France', 298),
 ('Canada', 253),
 ('Australia', 109),
 ('Spain', 69),
 ('Italy', 59),
 ('China', 58),
 ('Japan', 56),
 ('Hong Kong', 46),
 ('India', 38),
 ('Ireland', 37),
 ('Mexico', 29),
 ('Belgium', 25),
 ('Czech Republic', 24),
 ('New Zealand', 22),
 ('South Africa', 20),
 ('Denmark', 20),
 ('Switzerland', 18),
 ('South Korea', 18),
 ('Sweden', 18),
 ('Russia', 17),
 ('Netherlands', 17),
 ('United Arab Emirates', 14),
 ('Hungary', 13),
 ('Brazil', 13),
 ('Norway', 13),
 ('Romania', 11),
 ('Luxembourg', 11),
 ('Argentina', 9),
 ('Poland', 6),
 ('Iceland', 6),
 ('Israel', 6),
 ('Finland', 5),
 ('Austria', 5),
 ('Thailand', 5),
 ('Taiwan', 4),
 ('Morocco', 4),
 ('Bulgaria', 4),
 ('Iran', 4),
 ('Bahamas', 3),
 ('Malta', 3),
 ('Greece', 3),
 ('Jamaica', 2),
 ('Slovenia', 2),
 ('Pakistan', 2),
 ('Malaysia', 2),
 ('Peru', 2),
 ('Kazakhstan', 2),
 ('Chile', 2),
 ('Slovakia', 2),
 ('Colombia', 2),
 ('Dominica', 1),
 ('Monaco', 1),
 ('Tunisia', 1),
 ('Philippines', 1),
 ('Bosnia and Herzegovina', 1),
 ('Portugal', 1),
 ('Singapore', 1),
 ('Aruba', 1),
 ('Serbia', 1),
 ('Ukraine', 1),
 ('Panama', 1),
 ('Lithuania', 1),
 ('Cambodia', 1),
 ('Fiji', 1),
 ('Serbia and Montenegro', 1),
 ('Turkey', 1),
 ('Nigeria', 1),
 ('Cyprus', 1),
 ('Jordan', 1),
 ('Bolivia', 1),
 ('Ecuador', 1),
 ('Egypt', 1),
 ('Bhutan', 1),
 ('Lebanon', 1),
 ('Kyrgyz Republic', 1),
 ('Algeria', 1),
 ('Indonesia', 1),
 ('Guyana', 1),
 ('Guadaloupe', 1),
 ('Afghanistan', 1),
 ('Angola', 1),
 ('Dominican Republic', 1),
 ('Cameroon', 1),
 ('Kenya', 1)]


count_freq = pd.DataFrame(cont.most_common(10), columns=['countries', 'count'])
count_freq


#The barplot used to show the Top 10 production countries
plt.figure(figsize=(15,8))
plt.title('Top 10 production countries of movies', fontdict={'fontsize':14, 'color':'brown'})
sns.barplot(x='count', y='countries', data=count_freq);


# we first get the index of the movie for example Alice in Wonderland index is 32
movie_index = df[df['title'] == 'Alice in Wonderland'].index[0]
movie_index

32


# we get the similarities for the movie
distances = similarity[32] 
# we sort the similarities in descending order
sorted(distances,reverse=True)[0:10]

[1.0000000000000002,
 0.3360537729058417,
 0.2832856927186045,
 0.2783349703706405,
 0.26122949691608693,
 0.2545454545454546,
 0.24003840921845832,
 0.23994948963429277,
 0.2397457108377597,
 0.23472626340651012]


#then we get the top 3 movies and their Index and similarity score
num=3
movies_list = sorted(list(enumerate(distances)),reverse=True, key=lambda x:x[1])[1:num+1]
movies_list

[(525, 0.3360537729058417),
 (885, 0.2832856927186045),
 (135, 0.2783349703706405)]


# finally we access them using Index
for i in movies_list:
 print(df.iloc[i[0]].title)
 print(df.iloc[i[0]].movie_id)

Cars
920
The Book of Life
228326
Kung Fu Panda 3
140300


#generate random title from the list of movies
import random
def random_title():
    return random.choice(df['title'])

random_title()

'The Cookout'


movie_name='Alice in Wonderland'
popular_movies = recommend(movie_name) 
popular_movies


import pandas as pd
plt.rcParams['figure.figsize'] = (10, 9)
sns.barplot(popular_movies['Similarity Score'],popular_movies['title'],palette='tab20')
plt.show();

C:\Users\com\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.


import requests
from IPython.display import Image, HTML, display
def movie_display(popular_movies):
 getList_name = {}
 for x, xRows in popular_movies.iterrows():
    # we get the movie id from the dataframe and we use it to get the movie poster
    getResponse = requests.get('https://api.themoviedb.org/3/movie/{}?api_key=c0bda0be71f7815fd6ba2eb5f5c86fd8'.format(xRows['movie_id']) ) # every movie has a unique ID 
    getData = getResponse.json() # we request the data from the API and convert it to json
    
    # a bug fixed because sometimes there are is no poster so it returrns error
    if getData['poster_path']==None:
     continue
    else:   
     getPath = "http://image.tmdb.org/t/p/w500" + getData['poster_path']    # get the path of the poster
     getList_name[xRows['title']] = getPath
 
 
 display(HTML(f"""  < <div style="font-size:24px; font-weight:Bold; color:#fff; text-align:center; padding-top:8px; height:12%; width: 100%; border:1px solid #ccc; border-radius:10px; margin-top:10px; background-color:#FA1A1A;">{movie_name}</div> """))   
 # in here he loops on the number of movies to be recommended which is in num_recommend variable in recommend() function
 for i in range(0,popular_movies.shape[0],5):
  

  display( HTML(f"""
                    <table>
                    <tr>
                        <td><img src={list(getList_name.values())[i]} style='border-radius:10px; height:400px; width:575px; border:1px solid #999;'></td>
                        <td><img src={list(getList_name.values())[i+2]} style='border-radius:10px; height:400px; width:575px; border:1px solid #999;'></td>
                        <td><img src={list(getList_name.values())[i+3]} style='border-radius:10px; height:400px; width:575px; border:1px solid #999;'></td>
                        <td><img src={list(getList_name.values())[i+4]} style='border-radius:10px; height:400px; width:575px; border:1px solid #999;'></td>
                        
                    </tr>
                  
                        <td><div style="height:60px; padding-top:15px; text-align:center; font-size:14px; font-weight:bold; border:1px solid #ccc; border-radius:10px;">{list(getList_name.keys())[i+0]}</div></td>
                        <td><div style="height:60px; padding-top:15px; text-align:center; font-size:14px; font-weight:bold; border:1px solid #ccc; border-radius:10px;">{list(getList_name.keys())[i+2]}</div></td>
                        <td><div style="height:60px; padding-top:15px; text-align:center; font-size:14px; font-weight:bold; border:1px solid #ccc; border-radius:10px;">{list(getList_name.keys())[i+3]}</div></td>
                        <td><div style="height:60px; padding-top:15px; text-align:center; font-size:14px; font-weight:bold; border:1px solid #ccc; border-radius:10px;">{list(getList_name.keys())[i+4]}</div></td>
                    </tr>
                    
                </table>"""))


movie_display(recommend(movie_name,))


import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import seaborn as sns
import numpy as np
import warnings
warnings.filterwarnings('ignore')
from IPython.display import HTML


#we are reopening the files in new dataframes, this was just made because it was easier to collaborate this way 
df_score=pd.read_csv('tmdb_5000_movies.csv')
df_score2=pd.read_csv('tmdb_5000_credits.csv')


df_score = df_score.merge(df_score2,on='title')
#the title will be like our key that merges the columns to the correct position, just like the database forgein key


df_score.drop(['vote_count', 'movie_id', 'runtime','homepage','budget','revenue','overview','release_date','spoken_languages','status','tagline','title','original_language'], axis=1, inplace=True)


df_score.head(2)


#a set that takes all the empty rows we need to remove
empty_rows=set()
def json_to_list(col):
    # if crew we will parse from a specific key 
    if col == 'crew':
        # will move through the entire dataframe
        for i in range(0, len(df_score)):
            film_director =""
            # change the row content into a list
            df_list_ = eval(df_score.loc[i,col])
            for j in range(0,len(df_list_)):
                # parse the parts with directors only 
                if df_list_[j]['job'] == 'Director':
                    film_director = df_list_[j]['name']
                    df_score.loc[i,col] = str(film_director)
                    # break because we will not need to search more
                    break
            if film_director == "":
                # if we are empty then add to the empty set 
                empty_rows.add(i)
                df_score.loc[i,col] = ''
    elif col == 'cast':
        # loop through all the dataframe 
        for i in range(0, len(df_score)):
            # this list will hold the parsed data 
            list_ = []
            # we will convert the string list of dictionaries, into a list of dictionary that we can access
            df_list_ = eval(df_score.loc[i,col])
            # we will loop through this list of dictionary to extract the needed data 
            for j in range(0, len(df_list_)):
                temp_ = df_list_[j]["name"]
                list_.append(temp_.lower())
            # if there is nothing then we will add this to rows that need to be deleted 
            if len(df_list_) == 0:
                empty_rows.add(i)
            # we are only interested in the first 4 actors 
            selected = list_[0:4]
            #we add this to the row 
            df_score.loc[i,col] =  str(selected)
    else:
        for i in range(0, len(df_score)):
            #loop through the entire dataframe 
            list_ = []
            #we will take each content in the row and change it to a list of dictionaries to be able to parse 
            df_list_ = eval(df_score.loc[i,col])
            for j in range(0, len(df_list_)):
                # we will select the part of data we are interested in
                temp_ = df_list_[j]["name"]
                list_.append(temp_.lower())
            # this will happen when our list is empty, thus we will need to add it to the rows to be deleted 
            if len(df_list_) == 0:
                empty_rows.add(i)
            df_score.loc[i,col] = str(list_)


# we change the jason text to the list 
json_to_list('genres')
json_to_list('keywords')
json_to_list('production_companies')
json_to_list('production_countries')
json_to_list('cast')
json_to_list('crew')
# we delete the rows that have any empty rows or columns 
empty_rows = list(empty_rows)
df_score.drop(empty_rows, axis=0, inplace=True)


#we need to reset the index of the dataframe
df_score.reset_index(level=None, drop=True, inplace=True, col_level=0)


df_score


#function change to binary representations
def binary(genre_list, uniqueList):
    #this is the list that will contain the zeros and ones representing the presence or no presence of an entity 
    binaryList = []
    for genre in uniqueList:
        if genre in genre_list:
            binaryList.append(1)
        else:
            binaryList.append(0)
    
    return binaryList


#function that gets the unique value
def unique_list(col):
    # we used a set to not count any repeats 
    unique_set = set()
    for i in range(0, len(df_score)):
        #loop on the entire dataframe 
        l = df_score.iloc[i,col]
        for j in range(len(l)):
            #add the entities in the list to the set 
            unique_set.add(l[j])
    unique_list = list(unique_set)
    return unique_list


#converting all the cells that contain a  list of strings to a list of binary to be able to use it in the score predictor 

genreL = unique_list(0)
df_score['Genres_bin'] = df_score.iloc[:,0].apply(lambda x: binary(x,genreL))

castL = unique_list(8)
df_score['Actors_bin'] = df_score.iloc[:,8].apply(lambda x: binary(x,castL))

crewL = unique_list(9)
df_score['Directors_bin'] = df_score.iloc[:,9].apply(lambda x: binary(x,crewL))

keywordsL = unique_list(2)
df_score['Keywords_bin'] = df_score.iloc[:,2].apply(lambda x: binary(x,keywordsL))


df_score.head(3)


from scipy import spatial
#this is a function that uses cosine similarity to find how similar two movies are
# the similarity is upon
#1) genre
#2) Actors
#3) Directors
#4) Keywords

def Similarity(movieId1, movieId2):
    a = df_score.iloc[movieId1]
    b = df_score.iloc[movieId2]
    
    genresA = a['Genres_bin']
    genresB = b['Genres_bin']
    
    genreDistance = spatial.distance.cosine(genresA, genresB)
    
    scoreA = a['Actors_bin']
    scoreB = b['Actors_bin']
    scoreDistance = spatial.distance.cosine(scoreA, scoreB)
    
    directA = a['Directors_bin']
    directB = b['Directors_bin']
    directDistance = spatial.distance.cosine(directA, directB)
    
    wordsA = a['Keywords_bin']
    wordsB = b['Keywords_bin']
    wordsDistance = spatial.distance.cosine(wordsA, wordsB)
    # because we are adding 4 cosine similarity values the values will be between  0  and 4 inclusive 
    return genreDistance + directDistance + scoreDistance + wordsDistance


Similarity(3,234) #lets check similarity between any 2 random movies

0.9062687954957778


#This is a function that calculates the similarity of a choosen movie and compares it with the rest, and finds the top
# 10 most similar movies and gets the average rating 
def score_prdictor(movieID):
    #create a disctionary that key: the index of the row,  value: the similarity score
    similarity_dic = dict()
    #loop through the dataframe to get the similarity scores
    for i in range(0, len(df_score)):
        if i == movieID:
            #we don't want to consider the movieID that we chose itself, because it will already get a score of 0 
            continue
        temp_similarity =  Similarity(movieID,i)
        similarity_dic[i]= temp_similarity
    #change the dictionary to a list of tuples
    similarity_list = list(similarity_dic.items())
    #sort the list according to the similarity score 
    similarity_list = sorted(similarity_list, key = lambda x: x[1])[:10]
    #total_score is used so we can get the averahe vote prediction
    total_score = 0
    for i in similarity_list:
        temp = df_score.loc[int(i[0]), 'vote_average']
        total_score+= temp 
    return round(total_score/10,2)


score_prdictor(4)

6.64


# real will store the actual vote_average
#predicted will store the predicted vote_average using cosine similarity function explained above 
real =[]
predicted =[]

#we will test on the first 100 rows 
for i in range(0,100):
    real.append(df_score.loc[i,'vote_average'])
    predicted.append(score_prdictor(i))


# created a new dataframe to have the data of real and predicted 
df_accuracy = pd.DataFrame()
df_accuracy['Real']=real
df_accuracy['Predicted']=predicted


# we will calculate the percentage error 
percentage_error =[]
for i in range(0, len(df_accuracy)):
    approx = df_accuracy.loc[i,'Predicted']
    exact = df_accuracy.loc[i,'Real']
    error = (abs(approx-exact)/exact)*100
    percentage_error.append(round(error,2))

df_accuracy["Percentage Error%"] = percentage_error


df_accuracy.head(3)


Average_error = sum(percentage_error)/len(percentage_error)
print("Average Percentage Error: "+str(round(Average_error,2))+" %")

Average Percentage Error: 9.66 %


df_accuracy.plot( y=['Real','Predicted'], kind = 'line')
plt.show()


genres = pd.Series([categ for row in df['genres'] for categ in row])
#geners


#list of all genres in the column "with repetition"
all_genres=[]
for i in genres:
    all_genres.append(i)
#all_genres


#get all different geners in list
diff_genres = list( dict.fromkeys(all_genres) )
#print(diff_genres)


#cloud of words of all genres
import matplotlib.pyplot as plt
from wordcloud import WordCloud
my_list=diff_genres
#convert list to string and generate
unique_string=(" ").join(my_list)
wordcloud = WordCloud(width = 1000, height = 500).generate(unique_string)
plt.figure(figsize=(15,8))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()
plt.close()


from collections import Counter
cnt = Counter()

for text in all_genres:
    cnt[text] += 1
# See most common ten words
cnt.most_common()

[('Drama', 2172),
 ('Comedy', 1705),
 ('Thriller', 1248),
 ('Action', 1122),
 ('Romance', 865),
 ('Adventure', 763),
 ('Crime', 674),
 ('ScienceFiction', 525),
 ('Horror', 507),
 ('Family', 504),
 ('Fantasy', 410),
 ('Mystery', 335),
 ('Animation', 232),
 ('Music', 175),
 ('History', 152),
 ('War', 116),
 ('Documentary', 101),
 ('Western', 72),
 ('Foreign', 32),
 ('TVMovie', 5)]


#make a data frame of all words and there frequency 
import pandas as pd
word_freq = pd.DataFrame(cnt.most_common(), columns=['words', 'count'])
word_freq


plt.figure(figsize=(10,10))
p=sns.jointplot(x='count', y='words', data=word_freq);
p.fig.suptitle('Most common genres')

Text(0.5, 0.98, 'Most common genres')

<Figure size 720x720 with 0 Axes>


plt.figure(figsize=(10,8))
sns.histplot(x='count', y='words', data=word_freq);

import pylab as pl
pl.suptitle("Frequency of genres")

Text(0.5, 0.98, 'Frequency of genres')


from cycler import cycler
import matplotlib as mpl
COLOR = 'crimson'
mpl.rcParams['text.color'] = COLOR
mpl.rcParams['axes.labelcolor'] = COLOR
mpl.rcParams['xtick.color'] = COLOR
mpl.rcParams['ytick.color'] = COLOR
plt.figure(figsize=(10,8))
sns.pointplot(x='count', y='words', data=word_freq,color="darkblue");
import pylab as pl
pl.suptitle("Frequency of genres")

Text(0.5, 0.98, 'Frequency of genres')


plt.figure(figsize=(10,8))
sns.barplot(x='count', y='words', data=word_freq);
import pylab as pl
pl.suptitle("Frequency of genres")

Text(0.5, 0.98, 'Frequency of genres')


#arranging the rows according to the popularity in descending order
import pandas as pd
p= pd.DataFrame(df.sort_values(by=['popularity'], ascending=False).head(10))


plt.figure(figsize=(10,10))
sns.pointplot(x='original_title', y='popularity', data=p);
plt.xticks(rotation=90)
import pylab as pl
pl.suptitle("Top 10 popular movies")

Text(0.5, 0.98, 'Top 10 popular movies')


plt.figure(figsize=(10,8))
sns.histplot(x='original_title', y='popularity', data=p);
plt.xticks(rotation=90)
import pylab as pl
pl.suptitle("Top 10 popular movies")

Text(0.5, 0.98, 'Top 10 popular movies')


plt.figure(figsize=(10,8))
sns.barplot(x='original_title', y='popularity', data=p);
plt.xticks(rotation=90)
import pylab as pl
pl.suptitle("Top 10 popular movies")

Text(0.5, 0.98, 'Top 10 popular movies')


#Dataframe of years and movies 
year_df = pd.DataFrame(df['release_year'].value_counts().reset_index())
year_df.columns = ['year', 'movies']
year_df=pd.DataFrame(year_df.sort_values(by=['movies'], ascending=False).head(10))


plt.figure(figsize=(30,10))
sns.pointplot(x='year', y='movies', data=year_df);
plt.xticks(rotation=90)

(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
 [Text(0, 0, '2004'),
  Text(1, 0, '2005'),
  Text(2, 0, '2006'),
  Text(3, 0, '2008'),
  Text(4, 0, '2009'),
  Text(5, 0, '2010'),
  Text(6, 0, '2011'),
  Text(7, 0, '2013'),
  Text(8, 0, '2014'),
  Text(9, 0, '2015')])


#make actor dataframe that had four columns first_actor, second_actor, third_actor, and popularity
actor= pd.DataFrame(df_score['cast'])
#use the cast column and split it by commas
actor=actor['cast'].str.split(',', expand=True)
#there were extra empty columns 
actor=actor.drop(columns=[3, 4])
#remove "["
actor[0]=actor[0].str.replace("[","")
actor.columns = ['first_actor', 'second_actor', 'third_actor']
popularity = df_score["popularity"]
actor = actor.join(popularity)
actor


#group each actor column with the popularity
one=actor.groupby('first_actor', as_index=False).sum()
two=actor.groupby('second_actor', as_index=False).sum()
three=actor.groupby('third_actor', as_index=False).sum()


#name in each dataframe the two columns the same
one.columns = ['actors', 'popularity']
two.columns = ['actors', 'popularity']
three.columns = ['actors', 'popularity']


#combine all data frames together to have all actors in same column and each one has the popularity of his movies
frames = [one, two,three]
result = pd.concat(frames)
result=result.groupby('actors', as_index=False).sum()


#get the top actors
top_actors= pd.DataFrame(result.sort_values(by=['popularity'], ascending=False).head(10))


plt.figure(figsize=(10,10))
sns.pointplot(x='actors', y='popularity', data=top_actors);
plt.xticks(rotation=90)
import pylab as pl
pl.suptitle("Top ten actors")

Text(0.5, 0.98, 'Top ten actors')


plt.figure(figsize=(10,8))
sns.histplot(x='actors', y='popularity', data=top_actors);
plt.xticks(rotation=90)
import pylab as pl
pl.suptitle("Top ten actors")

Text(0.5, 0.98, 'Top ten actors')


plt.figure(figsize=(10,8))
sns.barplot(x='actors', y='popularity', data=top_actors);
plt.xticks(rotation=90)
import pylab as pl
pl.suptitle("Top ten actors")

Text(0.5, 0.98, 'Top ten actors')


df_score_no=pd.read_csv('tmdb_5000_movies.csv')
df_score_no[df_score_no['budget'] == df_score_no['budget'].max()]


df_score_no[['id','title','budget', 'revenue']].sort_values(['budget'], ascending=False).head(10).style.background_gradient(subset=['budget', 'revenue'], cmap='PuBu')


df_score_no[df_score_no['runtime'] == df_score_no['runtime'].max()]


df_score_no['release_date'] = pd.to_datetime(df_score_no['release_date'], infer_datetime_format=True)
df_score_no['release_day'] = df_score_no['release_date'].apply(lambda t: t.day)
df_score_no['release_weekday'] = df_score_no['release_date'].apply(lambda t: t.weekday())
df_score_no['release_month'] = df_score_no['release_date'].apply(lambda t: t.month)

df_score_no['release_year'] = df_score_no['release_date'].apply(lambda t: t.year if t.year < 2018 else t.year -100)


plt.figure(figsize=(20,12))
edgecolor=(0,0,0),
sns.countplot(df_score_no['release_month'].sort_values(), palette = "Dark2", edgecolor=(0,0,0))
plt.title("Movie Release count by Month",fontsize=20)
plt.xlabel('Release Month')
plt.ylabel('Number of Movies Release')
plt.xticks(fontsize=12)
plt.show()

	budget	genres	homepage	id	keywords	original_language	original_title	overview	popularity	production_companies	...	runtime	spoken_languages	status	tagline	title	vote_average	vote_count	movie_id	cast	crew
0	237000000	[{"id": 28, "name": "Action"}, {"id": 12, "nam...	http://www.avatarmovie.com/	19995	[{"id": 1463, "name": "culture clash"}, {"id":...	en	Avatar	In the 22nd century, a paraplegic Marine is di...	150.437577	[{"name": "Ingenious Film Partners", "id": 289...	...	162.0	[{"iso_639_1": "en", "name": "English"}, {"iso...	Released	Enter the World of Pandora.	Avatar	7.2	11800	19995	[{"cast_id": 242, "character": "Jake Sully", "...	[{"credit_id": "52fe48009251416c750aca23", "de...
1	300000000	[{"id": 12, "name": "Adventure"}, {"id": 14, "...	http://disney.go.com/disneypictures/pirates/	285	[{"id": 270, "name": "ocean"}, {"id": 726, "na...	en	Pirates of the Caribbean: At World's End	Captain Barbossa, long believed to be dead, ha...	139.082615	[{"name": "Walt Disney Pictures", "id": 2}, {"...	...	169.0	[{"iso_639_1": "en", "name": "English"}]	Released	At the end of the world, the adventure begins.	Pirates of the Caribbean: At World's End	6.9	4500	285	[{"cast_id": 4, "character": "Captain Jack Spa...	[{"credit_id": "52fe4232c3a36847f800b579", "de...
2	245000000	[{"id": 28, "name": "Action"}, {"id": 12, "nam...	http://www.sonypictures.com/movies/spectre/	206647	[{"id": 470, "name": "spy"}, {"id": 818, "name...	en	Spectre	A cryptic message from Bond’s past sends him o...	107.376788	[{"name": "Columbia Pictures", "id": 5}, {"nam...	...	148.0	[{"iso_639_1": "fr", "name": "Fran\u00e7ais"},...	Released	A Plan No One Escapes	Spectre	6.3	4466	206647	[{"cast_id": 1, "character": "James Bond", "cr...	[{"credit_id": "54805967c3a36829b5002c41", "de...
3	250000000	[{"id": 28, "name": "Action"}, {"id": 80, "nam...	http://www.thedarkknightrises.com/	49026	[{"id": 849, "name": "dc comics"}, {"id": 853,...	en	The Dark Knight Rises	Following the death of District Attorney Harve...	112.312950	[{"name": "Legendary Pictures", "id": 923}, {"...	...	165.0	[{"iso_639_1": "en", "name": "English"}]	Released	The Legend Ends	The Dark Knight Rises	7.6	9106	49026	[{"cast_id": 2, "character": "Bruce Wayne / Ba...	[{"credit_id": "52fe4781c3a36847f81398c3", "de...
4	260000000	[{"id": 28, "name": "Action"}, {"id": 12, "nam...	http://movies.disney.com/john-carter	49529	[{"id": 818, "name": "based on novel"}, {"id":...	en	John Carter	John Carter is a war-weary, former military ca...	43.926995	[{"name": "Walt Disney Pictures", "id": 2}]	...	132.0	[{"iso_639_1": "en", "name": "English"}]	Released	Lost in our world, found in another.	John Carter	6.1	2124	49529	[{"cast_id": 5, "character": "John Carter", "c...	[{"credit_id": "52fe479ac3a36847f813eaa3", "de...

	movie_id	title	cast	crew
0	19995	Avatar	[{"cast_id": 242, "character": "Jake Sully", "...	[{"credit_id": "52fe48009251416c750aca23", "de...
1	285	Pirates of the Caribbean: At World's End	[{"cast_id": 4, "character": "Captain Jack Spa...	[{"credit_id": "52fe4232c3a36847f800b579", "de...
2	206647	Spectre	[{"cast_id": 1, "character": "James Bond", "cr...	[{"credit_id": "54805967c3a36829b5002c41", "de...
3	49026	The Dark Knight Rises	[{"cast_id": 2, "character": "Bruce Wayne / Ba...	[{"credit_id": "52fe4781c3a36847f81398c3", "de...
4	49529	John Carter	[{"cast_id": 5, "character": "John Carter", "c...	[{"credit_id": "52fe479ac3a36847f813eaa3", "de...

	budget	id	popularity	revenue	runtime	vote_average	vote_count	movie_id
count	4.809000e+03	4809.000000	4809.000000	4.809000e+03	4807.000000	4809.000000	4809.000000	4809.000000
mean	2.902780e+07	57120.571429	21.491664	8.227511e+07	106.882255	6.092514	690.331670	57120.571429
std	4.070473e+07	88653.369849	31.803366	1.628379e+08	22.602535	1.193989	1234.187111	88653.369849
min	0.000000e+00	5.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	5.000000
25%	7.800000e+05	9012.000000	4.667230	0.000000e+00	94.000000	5.600000	54.000000	9012.000000
50%	1.500000e+07	14624.000000	12.921594	1.917000e+07	103.000000	6.200000	235.000000	14624.000000
75%	4.000000e+07	58595.000000	28.350529	9.291317e+07	118.000000	6.800000	737.000000	58595.000000
max	3.800000e+08	459488.000000	875.581305	2.787965e+09	338.000000	10.000000	13752.000000	459488.000000

	budget	genres	homepage	id	keywords	original_language	original_title	overview	popularity	production_companies	...	runtime	spoken_languages	status	tagline	title	vote_average	vote_count	movie_id	cast	crew
0	237000000	[{"id": 28, "name": "Action"}, {"id": 12, "nam...	http://www.avatarmovie.com/	19995	[{"id": 1463, "name": "culture clash"}, {"id":...	en	Avatar	In the 22nd century, a paraplegic Marine is di...	150.437577	[{"name": "Ingenious Film Partners", "id": 289...	...	162.0	[{"iso_639_1": "en", "name": "English"}, {"iso...	Released	Enter the World of Pandora.	Avatar	7.2	11800	19995	[{"cast_id": 242, "character": "Jake Sully", "...	[{"credit_id": "52fe48009251416c750aca23", "de...
1	300000000	[{"id": 12, "name": "Adventure"}, {"id": 14, "...	http://disney.go.com/disneypictures/pirates/	285	[{"id": 270, "name": "ocean"}, {"id": 726, "na...	en	Pirates of the Caribbean: At World's End	Captain Barbossa, long believed to be dead, ha...	139.082615	[{"name": "Walt Disney Pictures", "id": 2}, {"...	...	169.0	[{"iso_639_1": "en", "name": "English"}]	Released	At the end of the world, the adventure begins.	Pirates of the Caribbean: At World's End	6.9	4500	285	[{"cast_id": 4, "character": "Captain Jack Spa...	[{"credit_id": "52fe4232c3a36847f800b579", "de...
2	245000000	[{"id": 28, "name": "Action"}, {"id": 12, "nam...	http://www.sonypictures.com/movies/spectre/	206647	[{"id": 470, "name": "spy"}, {"id": 818, "name...	en	Spectre	A cryptic message from Bond’s past sends him o...	107.376788	[{"name": "Columbia Pictures", "id": 5}, {"nam...	...	148.0	[{"iso_639_1": "fr", "name": "Fran\u00e7ais"},...	Released	A Plan No One Escapes	Spectre	6.3	4466	206647	[{"cast_id": 1, "character": "James Bond", "cr...	[{"credit_id": "54805967c3a36829b5002c41", "de...
3	250000000	[{"id": 28, "name": "Action"}, {"id": 80, "nam...	http://www.thedarkknightrises.com/	49026	[{"id": 849, "name": "dc comics"}, {"id": 853,...	en	The Dark Knight Rises	Following the death of District Attorney Harve...	112.312950	[{"name": "Legendary Pictures", "id": 923}, {"...	...	165.0	[{"iso_639_1": "en", "name": "English"}]	Released	The Legend Ends	The Dark Knight Rises	7.6	9106	49026	[{"cast_id": 2, "character": "Bruce Wayne / Ba...	[{"credit_id": "52fe4781c3a36847f81398c3", "de...
4	260000000	[{"id": 28, "name": "Action"}, {"id": 12, "nam...	http://movies.disney.com/john-carter	49529	[{"id": 818, "name": "based on novel"}, {"id":...	en	John Carter	John Carter is a war-weary, former military ca...	43.926995	[{"name": "Walt Disney Pictures", "id": 2}]	...	132.0	[{"iso_639_1": "en", "name": "English"}]	Released	Lost in our world, found in another.	John Carter	6.1	2124	49529	[{"cast_id": 5, "character": "John Carter", "c...	[{"credit_id": "52fe479ac3a36847f813eaa3", "de...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
565	150000000	[{"id": 12, "name": "Adventure"}, {"id": 16, "...	http://www.shrek2.com/	809	[{"id": 378, "name": "prison"}, {"id": 2343, "...	en	Shrek 2	Shrek, Fiona and Donkey set off to Far, Far Aw...	47.320801	[{"name": "DreamWorks SKG", "id": 27}, {"name"...	...	93.0	[{"iso_639_1": "en", "name": "English"}]	Released	Once upon another time...	Shrek 2	6.7	2988	809	[{"cast_id": 29, "character": "Shrek (voice)",...	[{"credit_id": "57d85f1ac3a36878e90025c4", "de...
566	120000000	[{"id": 16, "name": "Animation"}, {"id": 12, "...	http://disney.go.com/disneyvideos/animatedfilm...	920	[{"id": 830, "name": "car race"}, {"id": 1926,...	en	Cars	Lightning McQueen, a hotshot rookie race car d...	82.643036	[{"name": "Walt Disney Pictures", "id": 2}, {"...	...	117.0	[{"iso_639_1": "en", "name": "English"}, {"iso...	Released	Ahhh... it's got that new movie smell.	Cars	6.6	3877	920	[{"cast_id": 13, "character": "Lightning McQue...	[{"credit_id": "52fe428dc3a36847f8027841", "de...
692	150000000	[{"id": 16, "name": "Animation"}, {"id": 10751...	http://movies.disney.com/chicken-little	9982	[{"id": 1357, "name": "fish"}, {"id": 1415, "n...	en	Chicken Little	When the sky really is falling and sanity has ...	47.973995	[{"name": "Walt Disney Pictures", "id": 2}, {"...	...	81.0	[{"iso_639_1": "en", "name": "English"}]	Released	When it comes to saving the world, it helps to...	Chicken Little	5.6	944	9982	[{"cast_id": 19, "character": "Chicken Little ...	[{"credit_id": "52fe4557c3a36847f80c8aff", "de...
1065	120000000	[{"id": 12, "name": "Adventure"}, {"id": 16, "...	http://movies.disney.com/a-bugs-life	9487	[{"id": 1442, "name": "winter"}, {"id": 1721, ...	en	A Bug's Life	On behalf of "oppressed bugs everywhere," an i...	87.350802	[{"name": "Walt Disney Pictures", "id": 2}, {"...	...	95.0	[{"iso_639_1": "en", "name": "English"}]	Released	An epic presentation of miniature proportions.	A Bug's Life	6.8	2303	9487	[{"cast_id": 1, "character": "Hopper (voice)",...	[{"credit_id": "52fe44fec3a36847f80b64e5", "de...
1658	100000000	[{"id": 28, "name": "Action"}, {"id": 12, "nam...	NaN	14164	[{"id": 3436, "name": "karate"}, {"id": 9715, ...	en	Dragonball Evolution	The young warrior Son Goku sets out on a quest...	21.677732	[{"name": "Ingenious Film Partners", "id": 289...	...	85.0	[{"iso_639_1": "en", "name": "English"}, {"iso...	Released	The legend comes to life.	Dragonball Evolution	2.9	462	14164	[{"cast_id": 17, "character": "Master Roshi", ...	[{"credit_id": "52fe45d29251416c75063b05", "de...

	budget	genres	homepage	id	keywords	original_language	original_title	overview	popularity	production_companies	...	runtime	spoken_languages	status	tagline	title	vote_average	vote_count	movie_id	cast	crew
0	15000000.0	[{"id": 28, "name": "Action"}, {"id": 12, "nam...	http://www.avatarmovie.com/	19995	[{"id": 1463, "name": "culture clash"}, {"id":...	en	Avatar	In the 22nd century, a paraplegic Marine is di...	150.437577	[{"name": "Ingenious Film Partners", "id": 289...	...	162.0	[{"iso_639_1": "en", "name": "English"}, {"iso...	Released	Enter the World of Pandora.	Avatar	7.2	11800	19995	[{"cast_id": 242, "character": "Jake Sully", "...	[{"credit_id": "52fe48009251416c750aca23", "de...
1	15000000.0	[{"id": 12, "name": "Adventure"}, {"id": 14, "...	http://disney.go.com/disneypictures/pirates/	285	[{"id": 270, "name": "ocean"}, {"id": 726, "na...	en	Pirates of the Caribbean: At World's End	Captain Barbossa, long believed to be dead, ha...	139.082615	[{"name": "Walt Disney Pictures", "id": 2}, {"...	...	169.0	[{"iso_639_1": "en", "name": "English"}]	Released	At the end of the world, the adventure begins.	Pirates of the Caribbean: At World's End	6.9	4500	285	[{"cast_id": 4, "character": "Captain Jack Spa...	[{"credit_id": "52fe4232c3a36847f800b579", "de...
2	15000000.0	[{"id": 28, "name": "Action"}, {"id": 12, "nam...	http://www.sonypictures.com/movies/spectre/	206647	[{"id": 470, "name": "spy"}, {"id": 818, "name...	en	Spectre	A cryptic message from Bond’s past sends him o...	107.376788	[{"name": "Columbia Pictures", "id": 5}, {"nam...	...	148.0	[{"iso_639_1": "fr", "name": "Fran\u00e7ais"},...	Released	A Plan No One Escapes	Spectre	6.3	4466	206647	[{"cast_id": 1, "character": "James Bond", "cr...	[{"credit_id": "54805967c3a36829b5002c41", "de...
3	15000000.0	[{"id": 28, "name": "Action"}, {"id": 80, "nam...	http://www.thedarkknightrises.com/	49026	[{"id": 849, "name": "dc comics"}, {"id": 853,...	en	The Dark Knight Rises	Following the death of District Attorney Harve...	112.312950	[{"name": "Legendary Pictures", "id": 923}, {"...	...	165.0	[{"iso_639_1": "en", "name": "English"}]	Released	The Legend Ends	The Dark Knight Rises	7.6	9106	49026	[{"cast_id": 2, "character": "Bruce Wayne / Ba...	[{"credit_id": "52fe4781c3a36847f81398c3", "de...
4	15000000.0	[{"id": 28, "name": "Action"}, {"id": 12, "nam...	http://movies.disney.com/john-carter	49529	[{"id": 818, "name": "based on novel"}, {"id":...	en	John Carter	John Carter is a war-weary, former military ca...	43.926995	[{"name": "Walt Disney Pictures", "id": 2}]	...	132.0	[{"iso_639_1": "en", "name": "English"}]	Released	Lost in our world, found in another.	John Carter	6.1	2124	49529	[{"cast_id": 5, "character": "John Carter", "c...	[{"credit_id": "52fe479ac3a36847f813eaa3", "de...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3703	6000000.0	[{"id": 35, "name": "Comedy"}, {"id": 18, "nam...	http://workingtitlefilms.com/film.php?filmID=59	712	[{"id": 213, "name": "upper class"}, {"id": 69...	en	Four Weddings and a Funeral	Four Weddings And A Funeral is a British comed...	29.834065	[{"name": "Channel Four Films", "id": 181}, {"...	...	117.0	[{"iso_639_1": "en", "name": "English"}]	Released	Five good reasons to stay single.	Four Weddings and a Funeral	6.6	632	712	[{"cast_id": 7, "character": "Charles", "credi...	[{"credit_id": "52fe426ec3a36847f801e2f3", "de...
3820	4000000.0	[{"id": 18, "name": "Drama"}, {"id": 10749, "n...	NaN	770	[{"id": 314, "name": "life and death"}, {"id":...	en	Gone with the Wind	An American classic in which a manipulative wo...	48.982550	[{"name": "Selznick International Pictures", "...	...	238.0	[{"iso_639_1": "en", "name": "English"}]	Released	The greatest romance of all time!	Gone with the Wind	7.7	970	770	[{"cast_id": 10, "character": "Scarlett O'Hara...	[{"credit_id": "52fe4274c3a36847f801fe01", "de...
3831	3500000.0	[{"id": 35, "name": "Comedy"}]	NaN	9427	[{"id": 1252, "name": "suicide attempt"}, {"id...	en	The Full Monty	Sheffield, England. Gaz, a jobless steelworker...	17.002623	[{"name": "Channel Four Films", "id": 181}, {"...	...	91.0	[{"iso_639_1": "en", "name": "English"}]	Released	The year's most revealing comedy.	The Full Monty	6.8	363	9427	[{"cast_id": 1, "character": "Gaz", "credit_id...	[{"credit_id": "52fe44f6c3a36847f80b45e9", "de...
4447	858000.0	[{"id": 16, "name": "Animation"}, {"id": 18, "...	http://movies.disney.com/bambi	3170	[{"id": 5774, "name": "forest"}, {"id": 10683,...	en	Bambi	Bambi's tale unfolds from season to season as ...	47.651878	[{"name": "Walt Disney Productions", "id": 3166}]	...	70.0	[{"iso_639_1": "en", "name": "English"}]	Released	A great love story.	Bambi	6.8	1405	3170	[{"cast_id": 9, "character": "Young Bambi (voi...	[{"credit_id": "52fe438cc3a36847f805ca73", "de...
4502	60000.0	[{"id": 27, "name": "Horror"}, {"id": 9648, "n...	http://www.blairwitch.com/	2667	[{"id": 616, "name": "witch"}, {"id": 3392, "n...	en	The Blair Witch Project	In October of 1994 three student filmmakers di...	41.690578	[{"name": "Artisan Entertainment", "id": 2188}...	...	81.0	[{"iso_639_1": "en", "name": "English"}]	Released	The scariest movie of all time is a true story.	The Blair Witch Project	6.3	1055	2667	[{"cast_id": 41, "character": "Mike", "credit_...	[{"credit_id": "52fe4364c3a36847f8050c01", "de...

	budget	genres	homepage	id	keywords	original_language	original_title	overview	popularity	production_companies	...	runtime	spoken_languages	status	tagline	title	vote_average	vote_count	movie_id	cast	crew
1014	0.0	[{"id": 27, "name": "Horror"}]	NaN	53953	[{"id": 10292, "name": "gore"}, {"id": 12339, ...	de	The Tooth Fairy	A woman and her daughter (Nicole Muñoz) encoun...	0.716764	[]	...	0.0	[{"iso_639_1": "en", "name": "English"}, {"iso...	Released	NaN	The Tooth Fairy	4.3	13	53953	[{"cast_id": 2, "character": "Peter Campbell",...	[{"credit_id": "52fe4885c3a36847f816b927", "de...
3117	0.0	[{"id": 18, "name": "Drama"}, {"id": 80, "name...	NaN	41894	[]	en	Blood Done Sign My Name	A drama based on the true story in which a bla...	0.397341	[]	...	0.0	[]	Released	No one changes the world alone.	Blood Done Sign My Name	6.0	5	41894	[{"cast_id": 0, "character": "Boo Tyson", "cre...	[{"credit_id": "58ba3af09251416073014bc1", "de...
3359	0.0	[{"id": 99, "name": "Documentary"}]	NaN	24977	[{"id": 6075, "name": "sport"}]	en	Michael Jordan to the Max	This documentary showcases basketball player M...	1.830306	[{"name": "IMAX", "id": 3447}]	...	46.0	[{"iso_639_1": "en", "name": "English"}]	Released	Up close some heroes get even bigger.	Michael Jordan to the Max	7.5	10	24977	[{"cast_id": 1, "character": "Himself", "credi...	[{"credit_id": "5786e26ac3a3685338000bdc", "de...
3408	0.0	[{"id": 10751, "name": "Family"}, {"id": 16, "...	NaN	294512	[]	en	Alpha and Omega: The Legend of the Saw Tooth Cave	The Alphas and Omegas share a thrilling advent...	1.874783	[{"name": "Crest Animation Production", "id": ...	...	53.0	[{"iso_639_1": "en", "name": "English"}]	Released	NaN	Alpha and Omega: The Legend of the Saw Tooth Cave	6.5	4	294512	[{"cast_id": 0, "character": "Runt", "credit_i...	[{"credit_id": "578f33d4c3a3686ecc00f86a", "de...
3476	6000000.0	[{"id": 99, "name": "Documentary"}]	NaN	57612	[{"id": 630, "name": "dolphin"}, {"id": 4676, ...	en	Dolphins and Whales: Tribes of the Ocean	This documentary goes to coral reefs of the Ba...	0.041651	[{"name": "3D Entertainment", "id": 5313}, {"n...	...	42.0	[{"iso_639_1": "en", "name": "English"}]	Released	NaN	Dolphins and Whales: Tribes of the Ocean	8.0	3	57612	[{"cast_id": 2, "character": "Narrator (voice)...	[{"credit_id": "52fe493dc3a36847f819012d", "de...
3631	5000000.0	[{"id": 99, "name": "Documentary"}]	NaN	78394	[{"id": 10506, "name": "prehistoric"}, {"id": ...	en	Sea Rex 3D: Journey to a Prehistoric World	Through the power of IMAX 3D, experience a won...	4.498368	[{"name": "N3D Land Productions", "id": 29943}...	...	41.0	[{"iso_639_1": "en", "name": "English"}]	Released	The T-Rex of the Seas come alive.	Sea Rex 3D: Journey to a Prehistoric World	5.9	11	78394	[{"cast_id": 1002, "character": "Conservatory ...	[{"credit_id": "556ca631c3a3685489006251", "de...
3677	0.0	[{"id": 35, "name": "Comedy"}, {"id": 18, "nam...	http://www.romeothemovie.com/	113406	[]	en	Should've Been Romeo	A self-centered, middle-aged pitchman for a po...	0.407030	[{"name": "Phillybrook Films", "id": 65147}]	...	0.0	[{"iso_639_1": "en", "name": "English"}]	Released	Even Shakespeare didn't see this one coming.	Should've Been Romeo	0.0	0	113406	[{"cast_id": 4, "character": "Joey", "credit_i...	[{"credit_id": "5617d84d92514166e2001e21", "de...
3816	4000000.0	[{"id": 35, "name": "Comedy"}, {"id": 10749, "...	NaN	158150	[]	en	How to Fall in Love	An accountant, who never quite grew out of his...	1.923514	[{"name": "Annuit Coeptis Entertainment Inc.",...	...	0.0	[{"iso_639_1": "en", "name": "English"}]	Released	NaN	How to Fall in Love	5.2	20	158150	[{"cast_id": 1, "character": "Annie Hayes", "c...	[{"credit_id": "52fe4bdd9251416c910e82a3", "de...
3960	0.0	[{"id": 10752, "name": "War"}, {"id": 18, "nam...	NaN	281230	[{"id": 187056, "name": "woman director"}]	en	Fort McCoy	Unable to serve in World War II because of a h...	0.384496	[]	...	0.0	[]	Released	NaN	Fort McCoy	6.3	2	281230	[{"cast_id": 0, "character": "Frank Stirn", "c...	[{"credit_id": "54e269aec3a368454b007976", "de...
3999	0.0	[]	NaN	346081	[]	en	Sardaarji	A ghost hunter uses bottles to capture trouble...	0.296981	[]	...	0.0	[]	Released	NaN	Sardaarji	9.5	2	346081	[]	[{"credit_id": "558ab3f4925141076f0001d7", "de...
4075	0.0	[]	NaN	371085	[]	en	Sharkskin	The Post War II story of Manhattan born Mike E...	0.027801	[]	...	0.0	[]	Released	NaN	Sharkskin	0.0	0	371085	[]	[]
4125	0.0	[]	NaN	325140	[]	en	Hum To Mohabbat Karega	Raju, a waiter, is in love with the famous TV ...	0.001186	[]	...	0.0	[]	Released	NaN	Hum To Mohabbat Karega	0.0	0	325140	[]	[]
4212	0.0	[{"id": 18, "name": "Drama"}, {"id": 80, "name...	http://www.imdb.com/title/tt1289419/	66468	[]	en	N-Secure	N-Secure is a no holds-barred thrilling drama ...	0.134560	[]	...	0.0	[]	Released	NaN	N-Secure	4.3	4	66468	[{"cast_id": 3, "character": "David Alan Washi...	[{"credit_id": "52fe473ec3a368484e0bca79", "de...
4217	0.0	[{"id": 10749, "name": "Romance"}]	NaN	74084	[]	hi	दिल जो भी कहे	During the British rule in India, several Indi...	0.122704	[{"name": "Entertainment One Pvt. Ltd.", "id":...	...	0.0	[{"iso_639_1": "en", "name": "English"}, {"iso...	Released	NaN	Dil Jo Bhi Kahey...	0.0	0	74084	[{"cast_id": 2, "character": "Shekhar Sinha", ...	[{"credit_id": "575d52eac3a3683168003910", "de...
4248	1500000.0	[{"id": 35, "name": "Comedy"}]	NaN	51820	[{"id": 10183, "name": "independent film"}]	en	The Salon	A Beauty shop owner finds romance as she strug...	2.028170	[]	...	0.0	[{"iso_639_1": "en", "name": "English"}]	Released	Where you get more than just a hair cut!	The Salon	3.5	1	51820	[{"cast_id": 2, "character": "Ricky", "credit_...	[{"credit_id": "52fe4805c3a36847f815475f", "de...
4319	0.0	[{"id": 53, "name": "Thriller"}, {"id": 27, "n...	NaN	107315	[{"id": 888, "name": "screenwriter"}]	en	Below Zero	When Jack (Edward Furlong) is in danger of mis...	1.365140	[]	...	0.0	[{"iso_639_1": "en", "name": "English"}]	Released	There's nothing scarier than a blank page.	Below Zero	4.4	12	107315	[{"cast_id": 1002, "character": "Jack / Frank"...	[{"credit_id": "52fe4a7dc3a36847f81d0e6b", "de...
4324	0.0	[{"id": 27, "name": "Horror"}]	NaN	310933	[]	en	Bleeding Hearts	Captured Hearts, an insane serial killer/horro...	0.100533	[]	...	0.0	[]	Released	NaN	Bleeding Hearts	2.0	1	310933	[{"cast_id": 1, "character": "Sheriff Wilson",...	[{"credit_id": "58645a32c3a36852ba0151a9", "de...
4328	0.0	[{"id": 99, "name": "Documentary"}]	NaN	102840	[]	en	Sex With Strangers	For some married couples, sex is an obsession ...	0.014406	[]	...	0.0	[]	Released	NaN	Sex With Strangers	5.0	1	102840	[]	[]
4334	0.0	[{"id": 27, "name": "Horror"}, {"id": 99, "nam...	NaN	202604	[{"id": 2626, "name": "exorcism"}]	en	The Vatican Exorcisms	Documentary following US film-maker Joe Marino...	0.447166	[]	...	0.0	[{"iso_639_1": "it", "name": "Italiano"}, {"is...	Released	The public were never meant to know	The Vatican Exorcisms	4.4	11	202604	[{"cast_id": 2, "character": "Himself", "credi...	[{"credit_id": "52fe4cccc3a368484e1c7333", "de...
4411	0.0	[{"id": 10751, "name": "Family"}, {"id": 35, "...	https://www.epicbuzz.net/movies/karachi-se-lahore	357441	[]	en	Karachi se Lahore	A road trip from Karachi to Lahore where 5 fri...	0.060003	[]	...	0.0	[{"iso_639_1": "ur", "name": "\u0627\u0631\u06...	Released	NaN	Karachi se Lahore	8.0	1	357441	[{"cast_id": 0, "character": "", "credit_id": ...	[]
4441	0.0	[{"id": 27, "name": "Horror"}]	NaN	323270	[{"id": 9706, "name": "anthology"}]	en	The Horror Network Vol. 1	Serial killers, ghostly phone calls, inner dem...	0.392658	[]	...	0.0	[]	Released	NaN	The Horror Network Vol. 1	5.0	2	323270	[{"cast_id": 0, "character": "Hal", "credit_id...	[{"credit_id": "5798c7b29251411838003dd6", "de...
4464	0.0	[]	NaN	279759	[]	en	Harrison Montgomery	Film from Daniel Davila	0.006943	[]	...	0.0	[]	Released	NaN	Harrison Montgomery	0.0	0	279759	[]	[]
4472	0.0	[{"id": 27, "name": "Horror"}, {"id": 878, "na...	NaN	211557	[]	en	Vessel	Vessel is the story of the passengers of Fligh...	0.322553	[{"name": "Baker's Dozen Productions", "id": 5...	...	14.0	[{"iso_639_1": "en", "name": "English"}]	Released	NaN	Vessel	5.9	8	211557	[{"cast_id": 1, "character": "Danny", "credit_...	[{"credit_id": "52fe4d97c3a368484e1f1ad7", "de...
4508	0.0	[{"id": 80, "name": "Crime"}, {"id": 18, "name...	NaN	263503	[]	en	Water & Power	Twin brothers nicknamed "Water" and "Power" fr...	0.350557	[]	...	0.0	[]	Released	NaN	Water & Power	3.0	1	263503	[{"cast_id": 2, "character": "Water", "credit_...	[{"credit_id": "5342e38cc3a368151e003aa9", "de...
4510	0.0	[]	NaN	331493	[]	en	Light from the Darkroom	Light in the Darkroom is the story of two best...	0.012942	[]	...	0.0	[]	Released	NaN	Light from the Darkroom	0.0	0	331493	[]	[]
4559	0.0	[]	NaN	380097	[]	en	America Is Still the Place	1971 post civil rights San Francisco seemed li...	0.000000	[]	...	0.0	[]	Released	NaN	America Is Still the Place	0.0	0	380097	[]	[]
4564	0.0	[{"id": 10402, "name": "Music"}, {"id": 27, "n...	http://www.thedevilscarnival.com/	285743	[{"id": 3473, "name": "carnival"}, {"id": 4344...	en	Alleluia! The Devil's Carnival	The Devil's Carnival: Alleluia! is the second ...	0.674398	[{"name": "Limb from Limb Pictures", "id": 590...	...	0.0	[]	Released	Hell ain't got a prayer.	Alleluia! The Devil's Carnival	6.0	2	285743	[{"cast_id": 11, "character": "Lucifer", "cred...	[{"credit_id": "53e40a4f0e0a262b8f0050c4", "de...
4570	0.0	[{"id": 18, "name": "Drama"}]	NaN	94072	[]	en	Straight Out of Brooklyn	A Special Jury Award winner at the Sundance Fi...	0.161517	[]	...	0.0	[]	Released	NaN	Straight Out of Brooklyn	4.3	4	94072	[]	[{"credit_id": "52fe49499251416c750c3167", "de...
4572	0.0	[]	NaN	325579	[]	en	Diamond Ruff	Action - Orphan, con artist, crime boss and mi...	0.165257	[]	...	0.0	[]	Released	NaN	Diamond Ruff	2.4	4	325579	[]	[]
4575	0.0	[]	http://mutualfriendsmovie.com/	198370	[]	en	Mutual Friends	Surprise parties rarely go well. This one is n...	0.136721	[]	...	0.0	[{"iso_639_1": "en", "name": "English"}]	Released	Surprise parties rarely go well.	Mutual Friends	0.0	0	198370	[{"cast_id": 3, "character": "Liv", "credit_id...	[{"credit_id": "52fe4d7b9251416c9111797f", "de...
4577	0.0	[]	NaN	328307	[]	en	Rise of the Entrepreneur: The Search for a Bet...	The world is changing faster than ever. Techno...	0.052942	[]	...	0.0	[]	Released	NaN	Rise of the Entrepreneur: The Search for a Bet...	8.0	1	328307	[]	[]
4587	0.0	[]	NaN	281189	[{"id": 187056, "name": "woman director"}]	en	Gory Gory Hallelujah	Four actors compete for the role of Jesus - a ...	0.033883	[]	...	0.0	[]	Released	NaN	Gory Gory Hallelujah	1.0	1	281189	[]	[{"credit_id": "55135b3f9251412be400021e", "de...
4590	0.0	[{"id": 27, "name": "Horror"}, {"id": 35, "nam...	NaN	189711	[]	en	Love in the Time of Monsters	Two sisters travel to a cheesy tourist trap wh...	0.133619	[{"name": "Red Cube Picture", "id": 27892}, {"...	...	0.0	[]	Released	NaN	Love in the Time of Monsters	5.0	2	189711	[{"cast_id": 1, "character": "Lou", "credit_id...	[{"credit_id": "52fe4d609251416c751392bb", "de...
4617	0.0	[]	NaN	162396	[]	en	The Big Swap	In this British drama, Ellen (Sorcha Brooks) a...	0.627763	[]	...	0.0	[{"iso_639_1": "en", "name": "English"}]	Released	NaN	The Big Swap	0.0	0	162396	[{"cast_id": 2, "character": "Sam", "credit_id...	[{"credit_id": "52fe4c59c3a36847f8229a57", "de...
4626	0.0	[{"id": 28, "name": "Action"}, {"id": 12, "nam...	NaN	47534	[{"id": 2792, "name": "boxer"}, {"id": 4076, "...	en	Fighting Tommy Riley	An aging trainer and a young fighter, both in ...	0.045429	[]	...	0.0	[{"iso_639_1": "en", "name": "English"}]	Released	NaN	Fighting Tommy Riley	5.3	4	47534	[{"cast_id": 3, "character": "", "credit_id": ...	[{"credit_id": "52fe4737c3a36847f8129b6b", "de...
4639	0.0	[]	NaN	300327	[]	en	Death Calls	An action-packed love story on the Mexican bor...	0.005883	[]	...	0.0	[]	Released	NaN	Death Calls	0.0	0	300327	[]	[]
4663	0.0	[]	NaN	320435	[]	en	UnDivided	UnDivided documents the true story of how a su...	0.010607	[]	...	0.0	[]	Released	NaN	UnDivided	0.0	0	320435	[]	[]
4664	0.0	[{"id": 27, "name": "Horror"}, {"id": 53, "nam...	NaN	150211	[{"id": 177972, "name": "bickering"}, {"id": 2...	en	The Frozen	After a harrowing snowmobile accident, a young...	1.084387	[]	...	0.0	[]	Released	NaN	The Frozen	4.2	14	150211	[{"cast_id": 1, "character": "The Hunter", "cr...	[{"credit_id": "58e7ed2bc3a3684aa4045117", "de...
4668	0.0	[{"id": 35, "name": "Comedy"}]	NaN	40963	[{"id": 10183, "name": "independent film"}]	en	Little Big Top	An aging out of work clown returns to his smal...	0.092100	[{"name": "Fly High Films", "id": 24248}]	...	0.0	[{"iso_639_1": "en", "name": "English"}]	Rumored	NaN	Little Big Top	10.0	1	40963	[{"cast_id": 0, "character": "Seymour", "credi...	[]
4715	0.0	[{"id": 16, "name": "Animation"}, {"id": 10751...	NaN	13187	[{"id": 65, "name": "holiday"}, {"id": 207317,...	en	A Charlie Brown Christmas	When Charlie Brown complains about the overwhe...	8.701183	[{"name": "Warner Bros. Home Video", "id": 5173}]	...	25.0	[{"iso_639_1": "en", "name": "English"}, {"iso...	Released	That's what Christmas is all about, Charlie Br...	A Charlie Brown Christmas	7.5	153	13187	[{"cast_id": 2, "character": "Freida (voice)",...	[{"credit_id": "52fe454b9251416c75051a75", "de...
4735	0.0	[{"id": 10751, "name": "Family"}]	NaN	272726	[]	en	Dude Where's My Dog?	Left home alone with his dog Harry, young Ray ...	0.283970	[]	...	0.0	[]	Released	NaN	Dude Where's My Dog?	0.0	0	272726	[{"cast_id": 6, "character": "", "credit_id": ...	[{"credit_id": "592bed26c3a36877bc07a7e0", "de...
4762	50000.0	[{"id": 27, "name": "Horror"}, {"id": 53, "nam...	http://www.cthulhulives.org/cocmovie/index.html	20981	[{"id": 1523, "name": "obsession"}, {"id": 303...	en	The Call of Cthulhu	A dying professor leaves his great-nephew a co...	1.777148	[{"name": "HPLHS", "id": 17827}]	...	47.0	[{"iso_639_1": "en", "name": "English"}]	Released	NaN	The Call of Cthulhu	6.9	41	20981	[{"cast_id": 4, "character": "The Man", "credi...	[{"credit_id": "52fe4407c3a368484e00b4a5", "de...

	budget	genres	id	keywords	original_language	original_title	overview	popularity	production_companies	production_countries	...	status	tagline	title	vote_average	vote_count	movie_id	cast	crew	release_year	duration_genres
2	15000000.0	[Action, Adventure, Crime]	206647	[spy, based on novel, secret agent, sequel, mi...	en	Spectre	A cryptic message from Bond’s past sends him o...	107.376788	[Columbia Pictures, Danjaq, B24]	[United Kingdom, United States of America]	...	Released	A Plan No One Escapes	Spectre	6.3	4466	206647	[{"cast_id": 1, "character": "James Bond", "cr...	[{"credit_id": "54805967c3a36829b5002c41", "de...	2015	2.0
4	15000000.0	[Action, Adventure, Science Fiction]	49529	[based on novel, mars, medallion, space travel...	en	John Carter	John Carter is a war-weary, former military ca...	43.926995	[Walt Disney Pictures]	[United States of America]	...	Released	Lost in our world, found in another.	John Carter	6.1	2124	49529	[{"cast_id": 5, "character": "John Carter", "c...	[{"credit_id": "52fe479ac3a36847f813eaa3", "de...	2012	2.0
5	15000000.0	[Fantasy, Action, Adventure]	559	[dual identity, amnesia, sandstorm, love of on...	en	Spider-Man 3	The seemingly invincible Spider-Man goes up ag...	115.699814	[Columbia Pictures, Laura Ziskin Productions, ...	[United States of America]	...	Released	The battle within.	Spider-Man 3	5.9	3576	559	[{"cast_id": 30, "character": "Peter Parker / ...	[{"credit_id": "52fe4252c3a36847f80151a5", "de...	2007	2.0
6	15000000.0	[Animation, Family]	38757	[hostage, magic, horse, fairy tale, musical, p...	en	Tangled	When the kingdom's most wanted-and most charmi...	48.681969	[Walt Disney Pictures, Walt Disney Animation S...	[United States of America]	...	Released	They're taking adventure to new lengths.	Tangled	7.4	3330	38757	[{"cast_id": 34, "character": "Flynn Rider (vo...	[{"credit_id": "52fe46db9251416c91062101", "de...	2010	2.0
7	15000000.0	[Action, Adventure, Science Fiction]	99861	[marvel comic, sequel, superhero, based on com...	en	Avengers: Age of Ultron	When Tony Stark tries to jumpstart a dormant p...	134.279229	[Marvel Studios, Prime Focus, Revolution Sun S...	[United States of America]	...	Released	A New Age Has Come.	Avengers: Age of Ultron	7.3	6767	99861	[{"cast_id": 76, "character": "Tony Stark / Ir...	[{"credit_id": "55d5f7d4c3a3683e7e0016eb", "de...	2015	2.0

title	Spectre	John Carter	Spider-Man 3	Tangled	Avengers: Age of Ultron	Harry Potter and the Half-Blood Prince	Batman v Superman: Dawn of Justice	Quantum of Solace	Pirates of the Caribbean: Dead Man's Chest	The Lone Ranger	...	On The Downlow	Sanctuary: Quite a Conundrum	Bang	Primer	Cavite	El Mariachi	Newlyweds	Signed, Sealed, Delivered	Shanghai Calling	My Date with Drew
title
Spectre	1.000000	0.055670	0.054447	0.015386	0.095730	0.046524	0.057438	0.265908	0.045980	0.017293	...	0.049568	0.000000	0.000000	0.000000	0.013937	0.032141	-0.036564	0.018282	0.019627	0.000000
John Carter	0.055670	1.000000	0.074092	0.037689	0.140694	0.056980	0.062531	0.108556	0.112628	0.028239	...	0.020236	0.000000	0.053722	0.016449	0.113798	0.118094	0.000000	0.014927	0.048075	0.000000
Spider-Man 3	0.054447	0.074092	1.000000	0.024574	0.122312	0.055728	0.122312	0.091003	0.073435	0.013809	...	0.000000	0.000000	0.078811	0.032174	0.044519	0.064166	0.000000	0.014599	0.047019	0.031083
Tangled	0.015386	0.037689	0.024574	1.000000	0.038886	0.031497	0.051848	-0.012859	0.000000	0.058537	...	0.000000	0.000000	0.044544	0.013639	0.000000	0.021760	0.000000	0.012377	0.039862	0.000000
Avengers: Age of Ultron	0.095730	0.140694	0.122312	0.038886	1.000000	0.039193	0.145161	0.080003	0.096837	0.043704	...	0.000000	0.000000	0.027714	0.050913	0.058706	0.108306	0.000000	0.000000	0.049602	0.065583
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
El Mariachi	0.032141	0.118094	0.064166	0.021760	0.108306	0.098693	0.027077	0.161165	0.097538	0.036684	...	0.000000	0.038292	0.104679	0.042735	0.226670	1.000000	0.000000	0.000000	0.124904	0.096334
Newlyweds	-0.036564	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.043561	0.000000	0.000000	0.022422	0.000000	1.000000	0.088235	0.000000	0.000000
Signed, Sealed, Delivered	0.018282	0.014927	0.014599	0.012377	0.000000	0.037424	0.015401	0.000000	-0.018493	0.041731	...	0.039873	0.021780	0.013231	0.032410	0.022422	0.000000	0.088235	1.000000	0.015788	0.046967
Shanghai Calling	0.019627	0.048075	0.047019	0.039862	0.049602	0.080354	0.016534	0.065609	0.039707	0.000000	...	0.000000	0.023383	0.071024	0.000000	0.120360	0.124904	0.000000	0.015788	1.000000	0.067229
My Date with Drew	0.000000	0.000000	0.031083	0.000000	0.065583	0.079682	0.016396	0.048795	0.000000	0.000000	...	0.000000	0.000000	0.028172	0.017252	0.059676	0.096334	0.000000	0.046967	0.067229	1.000000

	countries	count
0	United States of America	3830
1	United Kingdom	605
2	Germany	314
3	France	298
4	Canada	253
5	Australia	109
6	Spain	69
7	Italy	59
8	China	58
9	Japan	56

	movie_id	title	Similarity Score
0	920	Cars	0.336054
1	228326	The Book of Life	0.283286
2	140300	Kung Fu Panda 3	0.278335
3	332	Inspector Gadget	0.261229
4	13053	Bolt	0.254545
5	49519	The Croods	0.240038
6	10477	Driven	0.239949
7	17711	The Adventures of Rocky & Bullwinkle	0.239746
8	145220	Muppets Most Wanted	0.234726
9	20542	Delgo	0.234726
10	10996	Stuart Little 2	0.230170
11	12703	The Brown Bunny	0.227260
12	62206	30 Minutes or Less	0.224733
13	2270	Stardust	0.222475
14	788	Mrs. Doubtfire	0.220946

	genres	id	keywords	original_title	popularity	production_companies	production_countries	vote_average	cast	crew
0	['action', 'adventure', 'fantasy', 'science fi...	19995	['culture clash', 'future', 'space war', 'spac...	Avatar	150.437577	['ingenious film partners', 'twentieth century...	['united states of america', 'united kingdom']	7.2	['sam worthington', 'zoe saldana', 'sigourney ...	James Cameron
1	['adventure', 'fantasy', 'action']	285	['ocean', 'drug abuse', 'exotic island', 'east...	Pirates of the Caribbean: At World's End	139.082615	['walt disney pictures', 'jerry bruckheimer fi...	['united states of america']	6.9	['johnny depp', 'orlando bloom', 'keira knight...	Gore Verbinski
2	['action', 'adventure', 'crime']	206647	['spy', 'based on novel', 'secret agent', 'seq...	Spectre	107.376788	['columbia pictures', 'danjaq', 'b24']	['united kingdom', 'united states of america']	6.3	['daniel craig', 'christoph waltz', 'léa seydo...	Sam Mendes
3	['action', 'crime', 'drama', 'thriller']	49026	['dc comics', 'crime fighter', 'terrorist', 's...	The Dark Knight Rises	112.312950	['legendary pictures', 'warner bros.', 'dc ent...	['united states of america']	7.6	['christian bale', 'michael caine', 'gary oldm...	Christopher Nolan
4	['action', 'adventure', 'science fiction']	49529	['based on novel', 'mars', 'medallion', 'space...	John Carter	43.926995	['walt disney pictures']	['united states of america']	6.1	['taylor kitsch', 'lynn collins', 'samantha mo...	Andrew Stanton
...	...	...	...	...	...	...	...	...	...	...
4171	['drama']	124606	['gang', 'audition', 'police fake', 'homeless'...	Bang	0.918116	['asylum films', 'fm entertainment', 'eagle ey...	['united states of america']	6.0	['darling narita', 'peter greene', 'michael ne...	Ash Baron-Cohen
4172	['science fiction', 'drama', 'thriller']	14337	['distrust', 'garage', 'identity crisis', 'tim...	Primer	23.307949	['thinkfilm']	['united states of america']	6.9	['shane carruth', 'david sullivan', 'casey goo...	Shane Carruth
4173	['action', 'crime', 'thriller']	9367	['united states–mexico barrier', 'legs', 'arms...	El Mariachi	14.269792	['columbia pictures']	['mexico', 'united states of america']	6.6	['carlos gallardo', 'jaime de hoyos', 'peter m...	Robert Rodriguez
4174	['comedy', 'drama', 'romance', 'tv movie']	231617	['date', 'love at first sight', 'narration', '...	Signed, Sealed, Delivered	1.444476	['front street pictures', 'muse entertainment ...	['united states of america']	7.0	['eric mabius', 'kristin booth', 'crystal lowe...	Scott Smith
4175	['documentary']	25975	['obsession', 'camcorder', 'crush', 'dream girl']	My Date with Drew	1.929883	['rusty bear entertainment', 'lucky crow films']	['united states of america']	6.3	['drew barrymore', 'brian herzlinger', 'corey ...	Brian Herzlinger

	words	count
0	Drama	2172
1	Comedy	1705
2	Thriller	1248
3	Action	1122
4	Romance	865
5	Adventure	763
6	Crime	674
7	ScienceFiction	525
8	Horror	507
9	Family	504
10	Fantasy	410
11	Mystery	335
12	Animation	232
13	Music	175
14	History	152
15	War	116
16	Documentary	101
17	Western	72
18	Foreign	32
19	TVMovie	5

	first_actor	second_actor	third_actor	popularity
0	'sam worthington'	'zoe saldana'	'sigourney weaver'	150.437577
1	'johnny depp'	'orlando bloom'	'keira knightley'	139.082615
2	'daniel craig'	'christoph waltz'	'léa seydoux'	107.376788
3	'christian bale'	'michael caine'	'gary oldman'	112.312950
4	'taylor kitsch'	'lynn collins'	'samantha morton'	43.926995
...	...	...	...	...
4171	'darling narita'	'peter greene'	'michael newland'	0.918116
4172	'shane carruth'	'david sullivan'	'casey gooden'	23.307949
4173	'carlos gallardo'	'jaime de hoyos'	'peter marquardt'	14.269792
4174	'eric mabius'	'kristin booth'	'crystal lowe'	1.444476
4175	'drew barrymore'	'brian herzlinger'	'corey feldman'	1.929883

	id	title	budget	revenue
17	1865	Pirates of the Caribbean: On Stranger Tides	380000000	1045713802
1	285	Pirates of the Caribbean: At World's End	300000000	961000000
7	99861	Avengers: Age of Ultron	280000000	1405403694
10	1452	Superman Returns	270000000	391081192
4	49529	John Carter	260000000	284139100
6	38757	Tangled	260000000	591794936
5	559	Spider-Man 3	258000000	890871626
13	57201	The Lone Ranger	255000000	89289910
46	127585	X-Men: Days of Future Past	250000000	747862775
22	57158	The Hobbit: The Desolation of Smaug	250000000	958400000

	Real	Predicted	Percentage Error%
0	7.2	7.06	1.94
1	6.9	6.59	4.49
2	6.3	6.74	6.98

TMDB 5000 Movie Dataset (Movie Recommeddation System)

Types of Recommendation Systems :¶

1. Content Based Recommendation System :¶

2. Popularity Based Recommendation System :¶

3. Collaborative Recommendation System :¶

Workflow :¶

1. Data Collection :¶

2. Data PreProcessing :¶

3. Feature Extraction :¶

4. Find the Similarity :¶

5. User Input :¶

6. Use Cosine Similarity :¶

Import Libraries :¶

Describe the data :¶

How to deal with outliers data ?¶

Null values:¶

Drop some columns and rows with null value :¶

Correlation Matrix :¶

Change release_date to year to easily deal with column :¶

Make a new column for runtime types :¶

Genre Extraction function : from raw data for the creation of tags :¶

Function for extracting top(first) 8 actors from the movie :¶

Function to fetch the director of movie from the crew column :¶

Remove spaces between words :¶

Lower casing all the alphabets in the tags column :¶

Apply Steming to remove similarities/duplications in words list :¶

Convert text to matrix :¶

----------------------------------------------------------------------------------------------------------¶

Visulization :¶

Recommendation¶

How the recommender function works ?¶

Bar plot titles and similarity scores :¶

Machine Learning Model¶

Cleaning the data¶

Importing needed packages¶

Importing the csv files¶

Merging the two files together¶

Droping things with low correlation with vote_average¶

Change the json files to lists¶

Generate binary lists 🔢¶

Prediction Model🔢¶

Similarity Function 👀¶

Score Predictor 🔮¶

Our Accuracy 📉¶

Answering Questions¶

What are the movie genres available?¶

What are the top 10 rated movies and what do they have in common?¶

What are the number of movies per year?¶

The top 10 actors who contributed in most successsful movies¶

Which movie has the highest budget?¶

Which movie is longest movie?¶

In which month most movies are released from 1921 to 2017?¶

`Types of Recommendation Systems :`¶

1. `Content Based Recommendation System :`¶

2. `Popularity Based Recommendation System :`¶

3. `Collaborative Recommendation System :`¶

`Workflow :`¶

1. `Data Collection :`¶

2. `Data PreProcessing :`¶

3. `Feature Extraction :`¶

4. `Find the Similarity :`¶

5. `User Input :`¶

6. `Use Cosine Similarity :`¶

`Import Libraries :`¶

`Describe the data :`¶

`How to deal with outliers data ?`¶

`Null values:`¶

`Drop some columns and rows with null value :`¶

`Correlation Matrix :`¶

`Change release_date to year to easily deal with column :`¶

`Make a new column for runtime types :`¶

`Genre Extraction function : from raw data for the creation of tags :`¶

`Function for extracting top(first) 8 actors from the movie :`¶

`Function to fetch the director of movie from the crew column :`¶

`Remove spaces between words :`¶

`Lower casing all the alphabets in the tags column :`¶

`Apply Steming to remove similarities/duplications in words list :`¶

`Convert text to matrix :`¶

`Visulization :`¶

`Recommendation`¶

`How the recommender function works ?`¶

`Bar plot titles and similarity scores :`¶

`Machine Learning Model`¶